## 4 Conclusions
### Implications of the Research  
This research has profound implications at both theoretical and practical levels. In terms of industrial practice, considering the multi-state characteristics and complex environmental influences of the CNOOC system during ship sailing, the comprehensive method based on distance evaluation feature dimension reduction and the Weibull proportional failure rate model proposed in this paper can improve the quality of maintenance decision making during the whole operation of the equipment under the premise of ensuring the real-time reliability of the system. By integrating the dynamic effects of multiple environmental factors on the deterioration process of equipment, and using a deep Belief Network (DBN) to globally characterize the system failure mechanism and decay law, this study provides a feasible analysis tool and quantitative basis for the operation and maintenance management of offshore oil systems. This method not only finds and predicts the risk points of key components in time during the long voyage of the ship but also maximizes the availability of equipment and navigation safety under the condition of limited resource allocation. At the same time, the research results have targeted guiding value for industry practice, which can help shipping enterprises strike a balance between cost and safety, and provide a scientific basis for management decisions such as equipment selection and operation environment optimization. At the level of academic research, this study proposes a new path for fault mechanism mining and risk assessment of multi-state systems by integrating the dimensionality reduction method based on distance evaluation of feature vectors of high-dimensional fluctuation signals with the reliability analysis based on the Weibull proportional failure rate model. The traditional reliability research is mostly based on a single or a small number of characteristic indicators, which has difficulty fully capturing the potential sensitive information in high-dimensional complex signals. The dimensionality reduction strategy adopted in this study not only effectively compresses the feature space dimension but also retains the critical fault information, which makes the subsequent model training and analysis more accurate and stable. Combined with the application of the DBN model in multi-state system reliability analysis, this study provides a new idea and technical breakthrough for multi-state reliability modeling and analysis by quantifying the influence mechanism of environmental factors on the equipment failure process and decline law. In addition, this study deeply explored the impact of maintenance strategies on system availability and economic benefits under different work situations, which provided theoretical support for and empirical demonstration of the expansibility research of intelligent operation and fault diagnosis theory in the field of ocean engineering. The interdisciplinary characteristics of this study have certain implications for the fields of ship engineering, operations research, information science, and complex system analysis. By combining environmental evolution with the equipment decay process, a more comprehensive and dynamic reliability evaluation model is constructed, which provides a theoretical basis for subsequent research in application scenarios such as extreme ocean environments and large complex integrated systems of ships. Through the effective dimensionality reduction of high-dimensional features and the deep learning of multi-layer network structure, the feature extraction and state recognition method demonstrated in this study can further promote the development of accurate fault diagnosis and the whole life cycle maintenance management theory of complex industrial systems.