Project Overview: Maritime System Analysis and Optimization
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.
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.
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.
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.
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.