<p>This study develops a novel dynamic event-triggered fault-tolerant boundary control strategy for a flexible manipulator governed by partial differential equations (PDEs) and subject to actuator partial loss of effectiveness (PLOE), time delay, and dynamic uncertainties. First, a dynamic compensation scheme employing adaptive neural networks is developed to approximate the unknown dynamics of the system, thereby effectively handling time-delay effects, dynamic uncertainties, and potential actuator faults. Second, to reduce communication burden and computational load, a novel dynamic event-triggered mechanism is introduced. Furthermore, based on the backstepping technique, the neural network-based boundary control law is derived. Through Lyapunov-based analysis, the proposed control strategy guarantees the boundedness of all signals in the closed-loop system, suppresses elastic deformation, and ensures accurate tracking of the desired joint angle. Finally, the effectiveness of the proposed method is demonstrated through numerical simulation results.</p>