Max Robotics — Global Service Robot Directory + US Certification

About

Jinghui Pan is a researcher specializing in robotic systems, fault diagnosis, and intelligent control, with a particular focus on applying deep learning and advanced control theory to enhance the reliability and safety of robotic manipulators. Pan's most significant contribution lies in developing data-driven fault diagnosis frameworks for robot joints, most notably leveraging deep convolutional neural networks (DCNN) and deep residual neural networks (DRNN) to detect and classify sensor and actuator faults — including gain errors, offset errors, and malfunctions — with high precision. This foundational work, which has accumulated nearly 50 citations since its 2021 publication, established Pan as a credible voice in AI-driven robotics diagnostics. Building on this, Pan has advanced fault-tolerant control strategies using fractional-order sliding mode controllers to address instability challenges inherent in conventional sliding mode designs. Additionally, Pan has explored machine vision-based fault detection for manipulators operating in complex, real-world environments, tackling practical challenges such as poor lighting and texture variability. Collectively, Pan's research bridges the gap between theoretical fault detection methodologies and their practical deployment, offering valuable tools for engineers and researchers working to develop more robust, self-diagnosing robotic systems in industrial and demanding operational settings.