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

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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.