Datong Liu

Harbin Institute of Technology, Weihai Science and Technology Bureau

Papers

Total Citations

164

H-Index

About

Datong Liu is a prominent researcher specializing in fault detection, condition monitoring, and the application of artificial intelligence to industrial robotics and autonomous systems. His work has made significant contributions to the reliability engineering of industrial robots, with a particular focus on harmonic reducers — critical mechanical components whose failure can disrupt entire robotic operations. Liu's most impactful contribution, "Fault Detection of the Harmonic Reducer Based on CNN-LSTM With a Novel Denoising Algorithm" (2021), has garnered 127 citations, reflecting its substantial influence on the field. This work introduced an innovative deep learning framework combining convolutional neural networks and long short-term memory models, paired with a sophisticated denoising algorithm to address the challenges posed by complex internal structures operating under high-torque conditions. Building on this foundation, his subsequent research explored acoustic emission techniques for harmonic reducer fault detection, further diversifying the diagnostic toolbox available to engineers. Beyond industrial robotics, Liu has extended his expertise to unmanned and autonomous systems, developing embedded instruments capable of online condition monitoring in increasingly complex AI-integrated environments. His body of work positions him as a key contributor bridging machine learning, signal processing, and real-world mechanical reliability — making his research essential reading for engineers and scientists advancing intelligent industrial systems.