With the rapid advancements of computing power, data storage and processing capacity, health monitoring and fault diagnosis of physical machines in real-time are becoming more practical everyday. In this research project, we are developing a comprehensive pipeline of topics to measure physical plants, identify them, and propose solutions based on the system identifications. Our research interests and topics include but are not limited to:
1) Technologies to miniaturize conventional sensors
2) Distributed sensor system development and wireless data streaming
3) Hardware and software development of automated and self-preceptive physical plants
4) Development of novel sensors across various engineering domains
5) Data mining and system identification from high-dimensional time-series data
6) Real-time fault diagnosis algorithms
7) Construction of time-series anomalous data archive
8) Active maintenance of anomalous machines with robot intervention