Jianyu Long
Papers
6
Total Citations
249
H-Index
5
About
Jianyu Long is a researcher whose work spans intelligent fault diagnosis, deep learning, and manufacturing optimization, with particular expertise in industrial robotics and sustainable production systems. His most significant contributions lie in developing advanced machine learning architectures for diagnosing faults in multi-joint industrial robots — a critical challenge in ensuring safe and reliable automated manufacturing. His 2020 paper introducing an attitude data-based deep hybrid learning framework has garnered 139 citations, establishing him as a leading voice in robot condition monitoring. Long has pioneered the use of novel architectures, including multiscale convolutional capsule networks and sparse auto-encoders, to extract discriminative features from attitude sensor data — an innovative alternative to conventional vibration-based approaches. His 2022 work on capsule networks for fault diagnosis further consolidated this research direction with 81 citations. More recently, Long has expanded his scope into green manufacturing, tackling disassembly line balancing problems through mixed-integer programming and metaheuristic algorithms, addressing the growing need for efficient recycling and remanufacturing systems. Collectively, his research reflects a productive trajectory bridging intelligent diagnostics with optimization-driven sustainable manufacturing, making meaningful contributions to both industrial reliability and circular economy engineering.
Research Focus
Key Achievements
Top Papers
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