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About

Yuting Qiao is an emerging researcher specializing in condition monitoring, fault diagnosis, and vibration analysis of industrial robots and mechanical transmission systems. Their work sits at the intersection of signal processing, machine learning, and robotics, with a particular focus on developing innovative diagnostic methodologies for precision components such as RV (rotate vector) reducers — critical yet failure-prone elements in modern industrial robot systems. Qiao's most impactful contributions include pioneering multi-modal fusion approaches, notably the sound-vibration spectrogram fusion method and nonlinear spectrum feature fusion technique for RV reducer diagnosis, which have collectively garnered over 50 citations since 2023, reflecting rapid uptake within the robotics and condition monitoring communities. Their comprehensive review of industrial robot fault diagnosis techniques (2024) has further established them as a synthesizer of this growing field. Additional contributions span subspace modal identification to address robot vibration challenges, collision detection under dynamic disturbances, and belt transmission fault identification using asymmetric-dot-pattern fusion strategies. Through this body of work, Qiao is helping lay the diagnostic and monitoring foundations essential for reliable, autonomous manufacturing systems — making their research highly relevant to both academic audiences and industry practitioners advancing smart factory technologies.