Machine Learning Driven Recalibration of Models for Enhanced Accuracy

Accurate ship performance prediction is critical for optimising maritime operations, reducing fuel consumption, and ensuring on-time arrival. Recalibrating ship models through the incorporation of real-world data significantly enhances their accuracy and reliability. 

The digital twins of vessels used by Syroco Live are built through the assembly of models that represent the physics-driven behaviour of the different elements of the ship. This post drills into the innovative use of Grey Box Models, which combine a data-driven approach with domain-specific knowledge, using artificial intelligence and machine learning to improve ship performance predictions.

Leveraging Real-World Navigation Data

Real-world navigation data provides an accurate representation of ship performance in real-life, offering an accurate baseline. Key data sources include high-frequency ship sensor data, AIS (Automatic Identification System) data and weather/oceanic historical information. By utilising these comprehensive data sources, the models can accurately reflect true operational conditions, leading to more reliable and precise predictions.

Collecting real-world navigation data involves integrating various data sources, using methods such as direct data logging from on-board IoT systems, data collection through remote sensors, and accessing third-party data services. Of course there are challenges such as data heterogeneity and quality but they can be addressed using robust data integration tools and validation protocols.

Indeed, using only high-quality data is paramount for accurate performance predictions. Therefore, data cleaning consists of identification and rectification or erroneous records, missing values, and inconsistencies in the data set. Techniques such as outlier detection, error correction, and consistency checks are essential in maintaining data integrity. 

Combining Data-Driven Approaches and Domain Knowledge

Models that are purely data-driven use complex algorithms and machine learning techniques to predict ship performance based solely on data. These models are highly adaptable, capable of learning from vast amounts of real-world data to improve their predictive accuracy without relying on predefined physical equations.

Adding domain knowledge to the equation significantly boosts the predictive capabilities of these models. Insights from naval architecture and operational expertise, such as fluid dynamics, hydrodynamic simulation, engine performance, and environmental interactions, can then be integrated into the models, ensuring they reflect true operational situations.

Recalibrated Models 

Grey Box Models combine the strengths of both physics-induced ship models and data-driven approaches. By integrating traditional engineering principles with advanced machine learning algorithms, these models benefit from the accuracy of physical models and the adaptability of data-driven methods. This hybrid approach allows Grey Box Models to effectively capture the complexities of real-world vessel operations, providing more reliable and precise predictions of ship performance under varying conditions. The inclusion of domain knowledge further enhances their predictive power, making them a very valuable tool for optimising ship efficiency and crew decision-making.

The robustness of these models can be assessed based on their ability to handle diverse conditions and maintain accuracy. The recalibrated models are more robust and can adapt to a broad range of operational and environmental situations. By building accuracy metrics into the predictions, it is possible to measure and grade the predictions and their accuracy.