Intelligent Pump Fault Diagnosis for Vanadium Redox Flow Battery Using Deep Learning with Multi-Head Self-Attention

Vanadium redox flow batteries (VRBs) are a promising technology for large-scale energy storage because of their high safety, long cycle life, and flexible capacity design. However, pump malfunctions during operation may disturb electrolyte flow distribution, induce electrochemical instability, and, under severe conditions, accelerate stack degradation, thereby reducing system safety and operational reliability. Restricted by factors including the nonlinear coupling between sensor signals and operating conditions, as well as the intricate electrochemical processes triggered by pump faults, effective fault diagnosis for VRB pumps remains a prominent challenge. The paper proposes a novel Temporal Convolutional Network (TCN)–Long Short-Term Memory (LSTM)–Multi-Head Self-Attention (MATT) deep learning framework for intelligent pump fault diagnosis. The framework operates through three complementary stages. Comprehensive experimental validation is conducted using a purpose-built VRB fault experimental platform under various current conditions. The results show that the proposed model achieves diagnostic accuracies exceeding 90% for all three investigated pump fault types, namely bilateral pump fault, positive pump fault, and negative pump fault. Comparative analysis confirms that the proposed model significantly outperforms other architectures. The effectiveness of the MATT in enhancing temporal feature extraction and fault diagnosis accuracy for VRB systems is validated.