The SRIS Laboratory focuses on interpretable fault diagnosis, neuro-symbolic reasoning, and safe intelligent control. By integrating physical knowledge, temporal logic, machine learning, and control theory, we develop engineering intelligent systems that are understandable, trustworthy, and verifiable. Our current projects emphasize industrial time-series and multimodal foundation models, neuro-symbolic intelligence, mechanism-informed interpretable fault diagnosis, verifiably safe learning-based control, and autonomous robotics. Welcome to join us.
We convert heterogeneous engineering measurements into physically meaningful fault evidence, mechanism-consistent explanations, and credible health decisions under variable operating conditions.
We learn explicit temporal, causal, and logical structures from data so that model decisions can be interpreted, checked, revised, and reused as formal knowledge.
We develop learning-enhanced planning and feedback control methods that satisfy explicit temporal tasks, safety constraints, robustness requirements, and human-interaction objectives.
Vibration, force, vision, events, physical models, and operational context
Time-frequency structure, causal graphs, and temporal-logic formulas
Structured neural learning, robust optimisation, and uncertainty-aware reasoning
Diagnosis, maintenance decisions, motion planning, and safe feedback control
