Development of a new acoustic/elastic metamaterial with broadband vibration and acoustic wave isolation ability for fault detecting sensors

如何避免重大工伤事故的发生，是工程结构设计和维修中的一个重要问题。如果能够适当地监测结构的健康状况，这些故障是可以避免的。在这种情况下，预测性维护在防止这些故障方面发挥着重要作用。Nanoprecise Sci公司是一家在工程结构材料失效分析方面提供尖端解决方案的领先公司。他们开发了一种先进的预测性维护解决方案系统RotationLF，该系统实施基于人工智能的算法来分析无线传感器收集的振动和声音信号，以实现发动机、压缩机、泵和变速箱等旋转设备的早期故障检测。通过对传感器信号的适当建模和数据分析，可以识别结构的变形或退化，并用于预测潜在的故障。在旋转低频中，无线纳米级传感器安装在旋转设备上，接收来自被测结构的振动和声波信号。传感器数据的质量对结构健康状况的评估起着至关重要的作用。Nanoprecise Sci公司面临的一个紧迫挑战是如何提高旋转低频的频率分辨率，这直接关系到旋转设备中的准确故障识别。目前的项目旨在提供一种解决方案，以提高当前系统的频率分辨率。具体地说，将开发一种新的集成超材料振动过滤器。新的超材料滤光片不仅根据材料成分进行了定制，更重要的是通过局部结构的详细几何设计来实现所需的振动滤波，从而提高了频率分辨率。该项目的成果可以大大提高断层预测的准确性。

How to avoid severe industrial accidents is a major issue in the design and maintenance of engineering structures. These failures could have been avoided if the health condition of the structures could be properly monitored. In this context, predictive maintenance plays an important role in preventing these failures. Nanoprecise Sci Corp is a leading company offering cutting-edge solutions in material failure analysis for engineering structures. They have developed RotationLF, an advanced predictive maintenance solution system, which implements artificial intelligence based algorithms to analyze vibration and acoustic signals collected by wireless sensors for the realization of early stage fault detection in rotating equipment, such as engines, compressors, pumps and gearboxes. By proper modelling and data analysis of the sensor signals, deformation or degradation of the structures can be identified and used to predict potential failures.In the RotationLF, wireless Nanoprecise sensors are mounted on the rotating equipment to receive the vibration and acoustic wave signals from the measured structures. The quality of the sensor data plays a critical role in the evaluation of the health condition of the structures. An urgent challenge facing Nanoprecise Sci Corp is how to increase the frequency resolution of RotationLF, which is directly associated with the accurate fault identification in rotating equipment. The current project aims to provide a solution to improve the frequency resolution of the current system. Specifically, a new integrated metamaterial vibration filter will be developed. The new metamaterial filter is tailored not only by material composition but also, and more importantly, by the detailed geometrical design of the local structure to achieve the desired vibration filtering and, therefore, the improvement of frequency resolution. The outcomes of this project can lead to a considerable increase in the accuracy of fault prediction.