Dr. Chengju Dong | Mechanical Reliability | Best Researcher Award

Ministry of Industry and Information Technology | China

Chengju Dong is a researcher specializing in intelligent mechanical systems with a focus on smart robotics, deep learning, and Prognostics and Health Management (PHM). His work integrates advanced signal processing, data-driven modeling, and intelligent diagnostic frameworks to enhance the reliability and autonomy of modern machinery. With expertise grounded in mechanical design theory and computational intelligence, he has developed innovative methods for weak fault detection, tensor decomposition, and multi-modal feature extraction, contributing significantly to predictive maintenance and intelligent monitoring systems. His research aims to bridge the gap between theoretical models and real-world applications by designing algorithms capable of detecting early-stage faults in complex electromechanical systems, particularly rotating machinery and robotic platforms. Chengju Dong’s scholarly output includes 10 peer-reviewed documents that have collectively received 19 citations from 19 citing documents, reflecting a growing impact in the fields of machine health prediction and intelligent diagnostics. His current h-index is 2 based on Scopus data, and Google Scholar also reports consistent citation visibility aligned with these metrics. He continues to expand his research toward more interpretable deep learning models, robust tensor-based diagnostic frameworks, and adaptive PHM systems suitable for industrial environments. His contribution to the scientific community highlights his commitment to advancing predictive intelligence and enhancing machinery health evaluation through data-centric methodologies and engineering innovation.

Profile

Scopus

Featured Publications 

Dong, C., Wu, Y., & Jiang, H. (Year). A novel weak fault feature extraction method based on tensor decomposition model for bearings