Fan Jiang

Papers

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About

Fan Jiang is an emerging researcher whose work focuses on intelligent fault diagnosis and condition monitoring of mechanical systems, with particular emphasis on conveyor belt infrastructure. Their research applies advanced signal processing techniques to solve practical industrial challenges, notably developing innovative methods for detecting faults in conveyor belt idlers — critical components in mining, manufacturing, and logistics operations where undetected failures can lead to costly downtime and safety hazards. Jiang's most recognized contribution introduces a sophisticated diagnostic framework combining Improved Singular Value Decomposition (ISVD) with Time-Frequency Representation Entropy (TFRE), cleverly addressing the challenging problem of Doppler-distorted sound signals that arise when sensors move relative to rotating machinery. This approach represents a meaningful advance in non-contact, acoustic-based fault detection, offering practical advantages for real-world industrial inspection scenarios. While still in the early stages of their research career, with their 2025 publication already attracting citation attention, Jiang demonstrates a strong aptitude for bridging theoretical signal processing with applied mechanical diagnostics. Students and engineers working in predictive maintenance, acoustic fault detection, or industrial automation will find Jiang's methodological innovations particularly relevant to modern smart manufacturing challenges.