Papers
5
Total Citations
44
H-Index
4
About
Haibo An is a researcher specializing in fault diagnosis and condition monitoring of precision mechanical systems, with a particular focus on Rotate Vector (RV) reducers used in industrial robotics. His work addresses a critical challenge in modern manufacturing: the unpredictable failure of high-precision robotic components caused by long-term mechanical wear and abrasion. An's most significant contributions center on developing advanced diagnostic frameworks that combine acoustic emission (AE) sensing techniques with sophisticated machine learning models. Most notably, he has pioneered the application of Hidden Markov Models (HMM) to RV reducer fault detection — a meaningful innovation given that RV reducer faults are considerably more complex and difficult to detect than conventional rotating machinery failures such as bearing or gear defects. His body of work spans experimental fault diagnosis (2017, 13 citations), retrogressive degradation analysis (2018), and progressively refined HMM-based detection systems (2019, 2020), accumulating over 40 citations across his key publications. An's research holds strong practical relevance for industries relying on high-precision robotics, including mechanical and integrated circuit manufacturing. By improving the reliability and longevity of industrial robots through smarter maintenance strategies, his work contributes meaningfully to the broader field of intelligent condition monitoring and predictive maintenance.
Research Focus
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Top Papers
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