Chenze Zhu

China University of Mining and Technology

Papers

Total Citations

H-Index

About

Chenze Zhu is a researcher whose work lies at the intersection of intelligent fault diagnosis, acoustic signal processing, and industrial machinery health monitoring. Their most notable contribution is a novel denoising method for belt conveyor roller fault signals, published in 2024, which addresses a critical challenge in smart mining and material handling systems. In complex industrial environments, acoustic fault signals are often buried under severe coupling interference from long operational durations, numerous rollers, and harsh working conditions. Zhu’s method enhances the clarity and reliability of fault detection, enabling more accurate, non-invasive diagnostics for conveyor systems—a cornerstone of modern bulk material transport. This work has already garnered 6 citations shortly after publication, signaling its timely relevance. Zhu’s research is particularly impactful for advancing intelligent inspection technologies, reducing downtime, and improving safety in heavy industries. By tackling real-world signal degradation, Chenze Zhu is helping to bridge the gap between theoretical signal processing and practical, field-deployable condition monitoring solutions.