Yang Tan
Papers
1
Total Citations
4
H-Index
1
About
Yang Tan is a leading researcher in machinery prognostics and predictive maintenance, with a focus on advancing uncertainty quantification and multimodal data fusion for industrial systems. Their most-cited work introduces a stochastic modeling framework for machinery multimodal uncertainty-aware remaining useful life (RUL) prediction, addressing a critical gap in the field: the overreliance on unimodal data, which can yield biased or incomplete assessments. By developing methods that integrate diverse sensor modalities while rigorously quantifying prediction uncertainty, Tan’s research enhances the reliability of RUL forecasts, directly supporting the prevention of catastrophic equipment failures. This framework, published in 2025 and already garnering early citations, demonstrates Tan’s ability to tackle complex, real-world engineering challenges. Their contributions are particularly impactful for predictive maintenance strategies, offering a more robust foundation for decision-making in high-stakes industrial environments. Tan’s work stands out for its innovative fusion of stochastic modeling with practical application, making it essential reading for researchers and engineers seeking to improve machinery health management through data-driven, uncertainty-aware approaches.
Research Focus
Key Achievements
Top Papers
- 1