wmduggan41 / maritime Goto Github PK

Project Overview: Maritime System Analysis and Optimization

1. Predictive Maintenance for Mechanical Systems

• Objective: Develop a machine learning model to predict maintenance needs or potential failures in mechanical systems using historical data.
• Data: Engine performance metrics, propulsion system health, and auxiliary system functionality.
• Approach: Utilize regression or classification algorithms to identify patterns and predict future maintenance requirements.

2. Hull Integrity Analysis

• Objective: Analyze hull integrity data to predict potential failure points or determine optimal inspection intervals.
• Data: Corrosion rate, hull integrity (thickness and stress points), and watertight door status.
• Approach: Apply statistical analysis or machine learning techniques to identify trends and potential risk areas in hull integrity.

3. Energy Optimization

• Objective: Optimize energy consumption using data on power generation and usage.
• Data: Power consumption details, generator performance metrics.
• Approach: Develop algorithms to analyze energy flow and propose optimization strategies to reduce operational costs.

4. Fault Detection in Electrical Systems

• Objective: Implement a system for real-time monitoring and anomaly detection in electrical systems.
• Data: Electrical faults, generator performance, power consumption.
• Approach: Use anomaly detection algorithms to identify unusual patterns and potential faults in real-time.

5. Operational Efficiency Analysis

• Objective: Analyze various data points to find correlations with ship operational efficiency and identify improvement areas.
• Data: Engine performance, auxiliary system functionality, overall operational data.
• Approach: Employ statistical analysis or machine learning to uncover correlations and suggest efficiency enhancements.