Prognostics and health management (PHM) is an essential means to optimize resource allocation and improve the intelligent operation and maintenance (O&M) efficiency of marine systems and equipment (MSAE). PHM generally consists of four technical processes, namely health condition motoring (HCM), fault diagnosis (FD), health prognosis (HP), and maintenance decision (MD). In recent years, a large amount of research has been implemented in each process. However, there is not any systematic review that covers the technical framework comprehensively. This article presents a review of the framework of PHM in the marine field to fill the gap. First, the essential HCM methods, which are widely observed in the academic literature, are introduced systematically. Then, the commonly used FD approaches and their applications in MSAE are summarized, and the implementation process of intelligent methods is systematically introduced. After that, the technologies of HP have been reviewed, including the construction of health indicator (HI), health stage (HS) division, and popular remaining useful life (RUL) prediction approaches. Afterwards, the evolution of maintenance strategy in the maritime field is reviewed. Finally, the challenges of implementing PHM for intelligent ships are put forward.

Ship mechanical system health prognosis is one of the major tasks of ship intelligent operation and maintenance (O&M). However, current failure prediction methods are aimed at single pieces of equipment, and system-level monitoring remains an underexplored area. To address this issue, an integration method based on a synthesized health indicator (SHI) and dynamic hybrid prediction is proposed. To accurately reflect the changes in system health conditions, a multi-state parameter fusion method based on dynamic kernel principal component analysis (DKPCA) and the stacked autoencoder (SAE) is presented, along with construction of a system SHI. Taking into consideration that the system degradation process includes global degradation trends, local self-healing phenomena, and local interference, a dynamic hybrid prediction model is established after SHI decomposition. The performance of the proposed approach is applied to a ship fuel-oil system to show its effectiveness.

Scaffolding safety inspection in construction sites increasingly relies on 3D point cloud perception, yet automated checking remains limited because building code clauses target structural configurations rather than isolated components. This work addresses the grounding challenge of mapping clause level abstract construction concepts to concrete, verifiable structural instances in real world point clouds. We propose a hierarchical grounding pipeline that progressively elevates semantics from component level instance and semantic parsing to orientation aware member recognition, and finally to pose and spatial context aware capsule graph reasoning for structural functional concept inference. On real world scaffolding scans, our approach attains 0.9148 OA and 0.8713 Macro-F1 on concept reasoning and achieves 78.16% end-to-end correct grounding on Level-3 instances. Compared with direct grounding, it improves Instance F1@0.50 by +7.5%. The grounded concept instances provide explicit structural evidence to localize inspection targets, enabling more reliable and scalable clause-oriented safety checking in complex site layouts.