REQDGAN‐TUA: A Reflection Equivariant Quantum Graph Attention Framework With Tactical Unit Optimization for Intelligent Fault Diagnosis in Industrial Robotics

ABSTRACT The proper diagnosis of faults in industrial robotic motor drive control systems during operational time is essential for achieving optimal operational efficiency and rapid recovery from downtimes. Conventional and other deep learning (DL)‐based models face challenges when analyzing industrial environments because they lack effective feature extraction methods and are noise‐sensitive, exhibiting weak generalization capabilities. The research introduces a Reflection Equivariant Quantum Dynamic Graph Attention Network with Tactical Unit Algorithm (REQDGAN‐TUA) as a new solution for fault prediction and predictive maintenance functions. This framework collects sensory data through advanced pre‐processing and employs the Discrete Cosine–Krawtchouk–Tchebichef Transform (DCKTKT) to extract features and establish robust discriminating patterns. The Reflection Equivariant Quantum Dynamic Graph Attention Network (REQDGAN) utilizes quantum encoding to identify symmetries, while its graph attention mechanisms analyze changing inter‐component relationships, which enhances fault detection capabilities. The Tactical Unit Algorithm (TUA) function enables optimization of