DKABN : Knowledge Translation and Embedding for Efficient Fault Diagnosis of Trolley Mechanism on Ship-to-Shore Cranes

Liao, Xiaoqiang and Wang, Dong and Ming, Xinguo and Xia, Min (2025) DKABN : Knowledge Translation and Embedding for Efficient Fault Diagnosis of Trolley Mechanism on Ship-to-Shore Cranes. IEEE Transactions on Industrial Informatics, 21 (10): 10. pp. 7958-7969. ISSN 1551-3203

Abstract

Efficient fault diagnosis in ship-to-shore cranes (STSC) is vital for reliable cargo transport. However, deep neural networks (DNNs) lack transparency, hindering their explainability and interaction with experts during diagnostic decision-making. Currently, neural-symbolic systems increasingly focus on knowledge translation and embedding to enhance DNNs applicability for real-world fault diagnosis. Hence, this article introduces a deep knowledge-augmented belief network (DKABN), where knowledge translation and embedding are conducted to visualize the behavior of deep belief networks and integrate domain knowledge. Specifically, for stacked restricted Boltzmann machines (RBMs) layers, a novel activation-weighted logic RBM (AWL-RBM) is designed to fairly consider the contribution of each literal and reduce the inconsistencies between symbolic logic and RBMs. In the AWL-RBM, we formally prove that multiple literal groups can still be mapped into a violation rank function be capable of equalizing RBM energy minimization. Besides, translation and embedding of symbolic literals are conducted to interpret how RBMs work and fuse domain knowledge. For fully connected layers, a rule format like IF-THENs is translated and embedded to provide a semantic representation for diagnosis decision-making, and integrate domain knowledge. Finally, verified using an STSC testbed, DKABN demonstrates exceptional diagnostic performance and significant application potential.