## 5 Discussion  
This paper proposes an innovative approach to enhancing the reliability and safe operation of equipment by implementing a new model for the automated evaluation of the technical conditions of equipment using ML techniques. The objective is to provide early warning of operational deviations, eliminate human errors, notify upcoming maintenance stages in advance and reduce costs. In developing the algorithm, several methods were considered, including fault tree analysis, failure mode and effects analysis (FMEA) and fault detection using ML techniques. Using the SIPOC diagram, all these methods were integrated to construct the algorithm. The algorithm was designed to handle both continuous data flow from onboard the ship and offline analysis by exporting recorded data for shore-based analysis during and after a voyage. Based on specified requirements and development possibilities and through the integration of ML techniques, the algorithm facilitates the monitoring of operations and the evaluation of the functional state of naval installations by analyzing operational data to assist maintenance decision-making. The algorithm enhances the monitoring phase of onboard installations by alerting potential defects and their effects on the components. In addition, the proposed algorithm facilitates the visualization of trends and functional deviations, mapping them to possible failure modes. Scalability is achievable, accommodating the complexities of installations and enabling the integration of additional sensors. The scalability of the algorithm can be demonstrated  
Figure 7: Two-dimensional graphical representation for the selected parameters in the GRAPH module. and validated by Handling Large Datasets, which involves progressively introducing larger datasets by simulating extra sensor data or incorporating historical records into the monitoring process. Additionally, Validation Through Predictive Accuracy can assess the algorithm's capacity to identify potential defects and correlate them with failure modes as system complexity increases. Stress Testing for Real-Time Monitoring serves as a method to conduct stress tests by simulating a high volume of real-time sensor data from ship systems under various operational conditions, including peak loads and fault occurrences. The obtained results contribute to the field of maintenance for ship systems and installations in several ways: (1) the integration of risk and reliability analysis (FTA and FMECA), simulation tools and ML techniques is achieved using the SIPOC method; (2) a database has been created to identify fault precursors by analyzing operational parameter provided by ship sensor and calculated parameters for measuring the load and efficiency of system components; (3) the monitoring system and graphical analysis of operational data have been enhanced; (4) a fault matrix has been constructed to identify possible failure modes in naval installations; (5) the analysis of operational parameter trends is integrated with ML techniques to identify potential failure modes in naval installations. Finally, an algorithm was developed leveraging the research findings to enhance the current monitoring system and assist in maintenance decision-making for naval installations. The primary advantage lies in the reduced time required for diagnosing the root cause of failures through rapid system evaluation using operational data. However, testing and validating the proposed algorithm on a generic installation revealed potential limitations arising from the complexity of the employed methods and tools. Methodologically, two risk analysis methods (FTA and FMECA) were employed, which provided the best results in the naval field. The limitations of each method were minimized by integrating them into the research. Although the choice of installation for the case study was thoroughly justified, applying the algorithm to a single installation was a limitation of the study.