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About

Zongrui Jiang is a leading researcher in industrial robot predictive maintenance and intelligent manufacturing, with a focus on data-driven fault diagnosis and state evaluation. His work addresses critical challenges in the automotive manufacturing industry, where unexpected equipment downtime can cause significant production losses. Jiang’s most cited paper, "State Evaluation Method of Robot Lubricating Oil Based on Support Vector Regression" (2021, 8 citations), pioneers a machine learning approach to assess lubricant health, enabling proactive maintenance. He further advances the field with "Data-driven fault identification method of RV reducer used in industrial robot" (2024, 3 citations), which tackles the difficult task of diagnosing faults in sealed, complex mechanical systems under real-world conditions. His comprehensive review (2023, 2 citations) synthesizes predictive maintenance strategies for automotive manufacturing, while his innovative use of knowledge graphs (2023, 2 citations) streamlines maintenance decision-making by structuring robot component data. Jiang’s contributions are vital for the Industrial Internet of Things (IIoT), offering scalable solutions that reduce downtime and improve equipment reliability. His research is essential for engineers and researchers seeking to implement intelligent, data-driven maintenance in modern manufacturing.