TAKAN: A Multi-modal Attention Fusion Framework with Temporal-Adaptive KAN for Precise Fault Diagnosis in Axial Flow Fans

Axial flow fans, as critical components within thermal power plant cooling systems, significantly influence power generation efficiency and operational safety integrity. To address the challenges of multi-modal data fusion difficulties and insufficient precision in axial flow fan fault diagnosis, this paper proposes a fault diagnosis framework based on multi-modal attention fusion and Temporal-Adaptive KAN (TAKAN). First, a multi-scale graph convolutional network (MS-GCN) is employed to construct feature relationship representations both within and between modalities, and dynamic attention mechanisms achieve power-adaptive multi-modal fusion. Next, recursive connections, memory enhancement, and Lyapunov stability constraints are introduced into the Kolmogorov-Arnold Network (KAN) to develop a temporal dynamics adaptive KAN structure with nonlinear system modeling capabilities, effectively capturing the dynamic evolutionary characteristics of axial flow fan faults. Experimental results demonstrate that the proposed method achieves a comprehensive diagnostic accuracy of 99.63% under three typical operating conditions, representing improvements of 4.43%, 5.53%, 5.90%, 7.38%, and 7.75% compared to AKAN, KAN, BiLSTM-attention, Transformer, TCN, respectively. When external disturbances are applied to the model, the TAKAN model exhibits the lowest performance degradation at only 0.49%, indicating its exceptional robustness and practical engineering significance.