shuman cheng

China University of Mining and Technology

Papers

Total Citations

H-Index

About

Shuman Cheng is a researcher whose work focuses on the intersection of mechanical fault diagnosis and signal processing, with a particular emphasis on industrial conveyor systems. Their most notable contribution to date is a pioneering method for detecting faults in conveyor belt idlers, published in 2025. This approach integrates improved singular value decomposition (ISVD) with time-frequency ridge extraction (TFRE) to analyze Doppler-distorted sound signals, offering a non-invasive, real-time diagnostic solution for critical mining and material handling equipment. Though early in its citation impact, this work addresses a significant industrial challenge—reducing downtime and preventing catastrophic failures in bulk material transport. Cheng’s research demonstrates a strong command of advanced signal processing techniques and their practical application to mechanical systems. Their work is particularly relevant for students and researchers in condition monitoring, acoustics, and industrial automation, showcasing how novel algorithmic approaches can enhance the reliability of heavy machinery. As the field of predictive maintenance evolves, Cheng’s contributions provide a foundation for further innovation in acoustic-based fault detection.