## 3 Methodology
#### Fault Detection Algorithm  
This paper proposes a new fault detection approach, integrating risk analysis tools like FTA and FMECA, along with ML techniques, to enhance maritime maintenance practices. In addition, it introduces the Supplier Inputs Process Output Customer (SIPOC) diagram as a graphical representation tool to illustrate the inputs, utility and benefits of a maritime maintenance system. Originating from [PERSON]'s Total Quality Management principles, the SIPOC diagram visualizes processes as integrated entities, emphasizing the interconnectedness of various elements (Figure 2). This concept portrays manufacturing as a complete and integrated process rather than a series of disjointed elements, aiding in understanding the diverse applications and stages preceding and succeeding any action. To pinpoint real issues within the manufacturing process, SIPOC components include both internal and external customers, product requirements and the inputs and outputs of the process [59; 60]. The proposed approach effectively analyzes a problem and utilizes three primary data sources: operating parameters encompassing both normal and faulty operations, detailed maintenance history and the technical condition of components, including degradation over time, to estimate operating hours until failure [44]. By analyzing the recorded parameter values, it is possible to estimate values for future moments, the type of trend (long-term increase or decrease in values), seasonal phenomena (changes in values over equal time periods) and cyclical phenomena (fluctuations that do not always have the same duration, i.e., are not periodic). The proposed algorithm named FaultApp, tailored to meet the operational and maintenance requirements of a ship's crew, utilizes planned activities and data from onboard monitoring systems to efficiently manage information, provide insights and enhance operational and maintenance processes through five structured operational modules, which are integrated for comprehensive functionality as described in Algorithm 1. ```
1.While data are being imported (import_data):
2.Set MonitoringArray to the import_data;
3.Call the MONITOR function;
4.Call the GRAPH function. 5.If a deviation is detected:
6.Call the ANALYSIS function;
7.Call the DIAG function;
8.Call the LINK_PMS function;
9.endif;
10.end the while loop. ```  
**Algorithm 1** FaultApp  
Figure 2: The SIPOC diagram for integrating research methods. Algorithm 1 has a universal character, is applicable to monitoring all installations onboard a ship and contains five modules: MONITOR and GRAPH were enhanced based on similar monitoring systems onboard, while DIAG, ANALYSIS and LINK_PMS were developed to conduct a technical state analysis of shipboard installations. Particularly, for the implementation, testing and operation stages, an onboard ship installation (e.g., seawater cooling system) was chosen to validate the results. The algorithm utilizes operational parameters from sensors installed on the ship's system and calculates additional parameters by comparing input and output values for the same component. It also evaluates the efficiency of redundant system components, storing this information in a Monitoring Array used by the MONITOR, DIAG, and GRAPH modules. The algorithm transmits real-time data to a database, flags functional imbalances, and saves data for analysis, identifying defects and deviations from nominal values. The MONITOR module displays, in real-time, within the interface of the chosen installation, the values of the functional parameters obtained from the sensors. The DIAG module (2) provides a graphical representation of the evolution of the operating data values according to the user's preference over set time intervals. The deviation of these parameters lies between the minimum and maximum values accepted for the optimal operating regime. Any value exceeding the maximum or minimum limits (values exceeding 100%) indicates an abnormal functional state.