An intelligent monitoring system that detects subtle degradation patterns in ship engine operational data before conventional rule-based alerts would fire, reducing the risk of costly unplanned downtime.
Type
Anomaly Detection
Domain
Maritime Operations
Methods
Isolation Forest, PCA
Status
Completed
The Challenge
Maritime engine systems generate continuous streams of operational data across dozens of sensors. Conventional monitoring relies on static thresholds, triggering alerts only when individual readings exceed pre-set limits.
Threshold-based monitoring misses gradual degradation. A slow drift in the relationship between sensor readings can indicate developing problems long before any single metric breaches a hard limit. By the time a threshold alert fires, the damage is often significant.
Approach
01
Multivariate Data Exploration
Explored the sensor dataset to understand normal operational patterns, inter-variable correlations, and natural variation ranges under healthy operating conditions.
02
Feature Engineering
Engineered features capturing temporal dynamics and cross-sensor relationships. Applied normalisation and PCA to create a compact representation preserving anomalous variance.
03
Anomaly Detection Modelling
Applied Isolation Forest as the primary detection method, complemented with statistical methods for interpretable cross-validation of findings.
04
Analysis and Reporting
Produced a detailed analytical report characterising detected anomalies, mapping them to potential operational causes, and recommending monitoring improvements.
ANOMALY DETECTION
ISOLATION FOREST
Results
Early
Detection before threshold alerts
Multi-Sensor
Cross-variable relationship analysis
Interpretable
Anomalies mapped to operational causes
The system successfully identified anomalous operating periods that would not have triggered conventional threshold-based alerts. The PCA-based approach proved particularly effective at detecting multivariate anomalies where no single sensor was in breach but the overall pattern was abnormal.