Name of the participant: Huanzhuo Wu
Description of the IT-research project: With the advance of Industry 4.0, the physical world is being digitalized through Internet of Things (IoT) and Big Data. This in turn enables the monitoring, control, and optimization of industrial production processes by embedding computation and network. One of the most promising Industry 4.0 applications is Ultra-Reliable and Low-Latency Communications (URLLC) acoustic anomaly detection, which allows predictive detection of potential production failures to avoid machine damage and production downtime.
As an essential step in acoustic anomaly detection, the data analysis technique Blind Source Separation (BSS) faces transmission and computational challenges in IoT networks. This project, namely In-network Blind Source Separation enabled Acoustic Anomaly Detection for Ultra-Reliable and Low-Latency Communications Applications (Net-BliSS), will work on the integration of computing and networking to overcome the challenges on communication networks and analysis systems from BSS, in order to enable URLLC acoustic anomaly detection.
Software Campus partners: TU Dresden, Huawei
Implementation period: 01.01.2021 – 30.06.2022