Naipeng Li
Papers
5
Total Citations
143
H-Index
4
About
Naipeng Li is a leading researcher in intelligent fault diagnosis and prognostics for industrial machinery, with a particular focus on industrial robots and their critical components. His work addresses the pressing challenge of data decentralization in real-world manufacturing, where labeled fault data is scarce and costly to obtain. Li’s most impactful contribution is the development of a targeted transfer learning framework using distribution barycenter mediums, a method that has garnered 111 citations and enables robust fault diagnosis across decentralized data sources. He has also pioneered semi-supervised approaches, such as graph label propagation combined with discriminative feature enhancement, to diagnose faults in RV reducers—key components of industrial robots—without requiring extensive labeled datasets. More recently, Li has advanced the field by introducing stochastic modeling frameworks for multimodal, uncertainty-aware remaining useful life (RUL) prediction, allowing for informed fusion of diverse sensor data. His comprehensive review on condition monitoring and fault diagnosis of industrial robots, published in 2024, has already become a key reference for researchers and practitioners, underscoring his role as a thought leader in the domain.
Research Focus
Key Achievements
Top Papers
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- 2Condition monitoring and fault diagnosis of industrial robots: A review16 citations · 2024
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