DLCNN: A Deep Logic Convolutional Network for Interpretable Fault Diagnosis of Hoist Mechanism on Ship-to-Shore Cranes

The fault diagnosis of hoist mechanisms in ship-to-shore cranes (STSCs) is paramount for maintaining shipping schedules and ensuring personnel safety at ports. Although deep networks have achieved some success in diagnosing faults in hoist mechanisms, their opaque nature often precludes them from providing trustworthy explanations for their decisions. To address this problem, this article introduces a deep logic convolutional neural network (DLCNN), which incorporates two symbolic languages (confidence and classification rules) to visualize how convolutional neural networks (CNNs) work. Confidence rules are extracted from logic convolutions (LCs). In the LC, confidence rules are designed from three perspectives-information loss, the tradeoff between soundness and interpretability, and quantitative reasoning-to provide a comprehensive understanding of the feature learning and reasoning of stacked convolutions. Besides, classification rules are extracted from CNN's full-connected layers to elucidate implicit relationships between fault features and labels. Our experimental investigations on an STSC testbed demonstrate that DLCNNs have powerful performance in fault recognition, interpretability, and potential engineering value.