Research on a belt roller fault diagnosis method based on adaptive band-time KAN

Belt conveyor idlers frequently fail under high-load harsh conditions, causing system shutdowns. Existing deep learning-based fault diagnosis methods suffer from insufficient frequency resolution and poor dynamic adaptability. To address this, this paper proposes a fault diagnosis framework based on adaptive frequency-band KAN: First, an adaptive frequency-band Mel filter bank designed based on fault mechanisms enhances resolution in critical fault frequency bands through non-uniform frequency-axis remapping and third-order peak detection. Second, a temporal convolutional network is integrated to expand the receptive field and capture cross-period features. A Kolmogorov-Arnold Networks (KAN) layer is introduced to dynamically analyze nonlinear coupling relationships in the frequency domain using learnable B-spline basis functions. This model achieves synergistic optimization of feature resolution enhancement and dynamic modeling, significantly improving diagnostic accuracy and cross-condition generalization capability for roller faults. Under actual conveyor roller operating conditions, fault prediction accuracy reaches 81.25%, fully validating the model's adaptability to real-world industrial scenarios.