## 3 Experimental Results
### Comparison with Existing Methods  
In order to further verify the effectiveness and adaptability of the proposed method, it is necessary to systematically compare it with the existing mainstream equipment fault diagnosis and health assessment methods. In recent years, the health diagnosis method combining feature engineering, dimension reduction technology, and multi-model combination has achieved remarkable results in the operation and maintenance decision making of industrial equipment. For example, [PERSON] proposed a health diagnosis method for dry press rollers in the paper industry, which combines deep neural networks, principal component analysis (PCA), k-means clustering, and Hidden Markov Models (HMMs) [35]. The three-state health classification of equipment (normal, early warning, fault) is successfully implemented, and the diagnostic ability of the method under the condition of a small sample size and its generalization among multiple types of equipment are verified.  
Compared with these methods, the proposed system reliability assessment framework integrating feature dimension reduction, the proportional hazards model (PHM), a Dynamic Bayesian Network (DBN), and the cloud center of gravity model has the following significant advantages. Firstly, at the feature processing level, we also use dimensionality reduction techniques to improve modeling efficiency and reduce information redundancy. However, compared with the traditional PCA method, the dimensionality reduction strategy used in this paper focuses more on maintaining the physical meaning of key reliability variables, which is helpful for the interpretability of subsequent models. Secondly, in the aspect of fault modeling, HMM usually assumes that the state transition is a fixed Markov process, which has difficulty dynamically integrating the influence of different equipment states and maintenance strategies. On the other hand, dynamic Bayesian networks have the ability to flexibly express the evolution characteristics of multi-state devices over time, especially suitable for the temporal reasoning and uncertainty modeling of complex systems. In addition, the cloud center of gravity model introduced in this paper makes the fuzzy assessment of the system health state possible, and makes up for the discrimination difficulty of the pure classification model in the face of fuzzy boundaries (such as \"critical state\"), thus improving the accuracy and robustness of the system state assessment.  
Nevertheless, the method in this paper has some limitations. Firstly, the dynamic Bayesian network model has a strong dependence on prior knowledge in the process of structure learning and parameter training. If the system structure is complex or the sensor coverage is insufficient, the performance of the model may be limited. Secondly, although the cloud model can describe the fuzziness well, its parameter tuning process is complex and easily affected by the sample size and quality. In addition, compared with some end-to-end deep learning methods, the proposed method may put forward higher requirements for computing resources and model integration at the deployment level, and it is necessary to trade off accuracy and complexity in practical applications.