The SRSL team recently received an award from the National Science Foundation (NSF) to investigate probabilistic and explainable deep learning for the intuitive predictive maintenance of industrial and agricultural equipment. The approach will not only predict the remaining useful life of a machine component, but it will also quantify the uncertainty of a prediction. As a result, maintenance decisions can be made from a risk-based perspective, eliminating unnecessary maintenance stemming from low-confidence predictions. This STTR Phase I project is in collaboration with a local-to-Iowa industry partner, Percēv (a subsidiary of Grace Technologies) and Grace, and is set to be completed in November 2021.
Rising Stars Spotlight: Venkat Nemani and Jinqiang Liu
Congrats to Venkat and Jinqiang for being recognized as “Rising Stars” by The REMADE Institute. Both have made outstanding contributions to our REMADE project titled “Data-Driven Design Support for Re-X of High-Value Components in Industrial and Agricultural Equipment”. Read more about their work on this project through the links below:
Venkat and Jinqiang – Well done and well deserved! Congratulations!