PREDICTIVE INFORMATICS RESEARCH LAB
Welcome
Advanced and Intelligent Manufacturing Systems (AIMS):
My research lab is focused on developing physics-informed AI/ML models for analyzing and assessing the performance and health condition of engineering assets within intelligent manufacturing systems and complex engineering environments, providing predictive and adaptive decision-support tools for optimal operations, maintenance, and control. Key focus areas include:
- AI-enabled digital manufacturing and cyber manufacturing systems
- Diagnostics and prognostics for advanced manufacturing processes and systems
- Data-driven optimal design for new materials fabrication processes, including 2D materials synthesis, nanomaterials synthesis
- Sensor data analytics and multi-modal data fusion for manufacturing process monitoring and control
- Roll-to-roll process for flexible electronics printing
- High-precision manufacturing processes, such as cold spray, wire-arc additive mfg. and gravure printing
- Manufacturing system design and optimization in sectors such as automotive assembly line, and semiconductor fabrication
Research Highlights
PyTSC: Traffic Signal Control Environment for Multi-Agent Reinforcement Learning
Rohit Bokade, Xiaoning Jin
We are pleased to announce that the MARL test platform for the Traffic Signal Control (TSC) environment is now available for research purposes, including testing and benchmarking.
Fleet Learning: Active learning-assisted semi-supervised learning for fault detection and
diagnostics with imbalanced dataset
Xiaomeng Peng, Xiaoning Jin, Shiming Duan, and Chaitanya Sankavaram
Data-driven Fault Detection and Diagnostics (FDD) methods often assume that sufficient labeled samples are class-balanced and faulty classes in testing are precedent or seen previously during model training. When monitoring a large fleet of assets at scale, these assumptions may be violated:
(I) only a limited number of samples can be manually labeled due to constraints of time and/or cost; (II) most of the samples collected in the engineering systems are under normal conditions, leading to a highly imbalanced class distribution and a biased prediction model.
Research Projects
Contact
Prof. Xiaoning “Sarah” Jin
Email: xi.jin@northeastern.edu
359 Snell Engineering Center
360 Huntington Ave. Boston, MA 02115