Liansheng Liu

Papers

Total Citations

162

H-Index

About

Liansheng Liu is a leading researcher in the field of industrial robotics reliability, with a primary focus on the fault detection and condition monitoring of harmonic reducers—critical components whose failure can severely disrupt robotic operations. His major contributions center on developing advanced diagnostic methodologies that combine signal processing with deep learning. Notably, his most cited work introduces a novel denoising algorithm integrated with a CNN-LSTM model, achieving robust fault detection in harmonic reducers under challenging, high-torque conditions. This paper has garnered 127 citations, underscoring its influence on predictive maintenance research. In subsequent work, Liu pioneered a fault detection method using acoustic emission signals, featuring two unique algorithms that enhance sensitivity to early-stage degradation. With over 160 combined citations, his research directly addresses the high failure rates of harmonic reducers in industrial robots, offering practical solutions for improving system availability. Liu’s work is essential reading for engineers and researchers advancing intelligent manufacturing and machinery health management.