Predictive Maintenance for Container Ships
Implemented condition monitoring system for critical ship machinery, preventing 18 potential failures and achieving 99.2% fleet availability.
The Challenge
A maritime shipping company was experiencing unexpected equipment failures on their container vessels, resulting in costly delays and emergency port stops. Each unplanned maintenance event cost hundreds of thousands in lost revenue and repair expenses.
With limited insight into machinery health, the company relied on time-based maintenance schedules that were either too frequent or missed critical failures, impacting fleet reliability and customer commitments.
Key Pain Points
- Unexpected engine and auxiliary system failures
- High costs from emergency repairs and vessel delays
- No early warning system for machinery degradation
- Inefficient scheduled maintenance approach
Our Solution
Condition Monitoring
Deployed vibration, temperature, and oil analysis sensors on engines, pumps, and critical auxiliary systems.
ML Failure Prediction
Built machine learning models trained on historical failure data to predict equipment issues weeks in advance.
Maintenance Planning
Integrated alerts with port schedules to plan maintenance during scheduled stops, avoiding emergency situations.
Results & Impact
Failures Prevented
Maintenance Cost Reduction
Fleet Availability
Additional Benefits
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