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In continuation to the following articles:
- Finding the Right Course: Balancing AI and Traditional Approaches in Maritime Solutions. Part 1
- Finding the Right Course: Balancing AI and Traditional Approaches in Maritime Solutions. Part 2
We want to provide you with our additional considerations and insights.
The maritime industry continues to evolve, driven by the need for efficiency, sustainability, and innovation. At Vector Software, we are committed to exploring AI’s potential while maintaining a balanced approach that incorporates proven, traditional methodologies. Our latest initiative brings us one step closer to intelligent, data-driven decision-making for vessel operations.
Real-Time Data Collection: The First Step Toward Smarter Consumption
Recognizing the importance of resource management in maritime operations, Vector Software has recently delivered a solution that enables real-time monitoring of vessels’ water consumption. By continuously collecting and analyzing data, this system provides operators with a clear and immediate understanding of usage patterns, helping to enhance transparency and efficiency. This capability lays the foundation for more advanced applications of AI in the maritime sector.
Next Phase: Detecting Anomalies with AI
With real-time data collection in place, the next logical step is to leverage ML and AI-driven anomaly detection. By analyzing historical and live data streams, AI can identify deviations in consumption that may indicate leaks, inefficiencies, or operational issues. Early detection of such anomalies will allow crews to take proactive measures, reducing waste, lowering costs, and ensuring uninterrupted water availability onboard. This case leads us to think about a general predictive maintenance solution that can help prevent unplanned downtime and increase maintenance productivity through better planning and problem detection in early stages. However, there are several challenges:
- AI/ML models were better suited for numerical data processing. It was difficult to derive meaningful information from minimal text, such as maintenance records, which are typically brief and lack detail.
- Anomaly Detection algorithms work like black boxes, and even experienced operators may find it challenging to draw the correct conclusions from the data.
The recent boost of AI in general and especially Generative AI enables a lot of new opportunities in the development of the solution mentioned above and can effectively address the mentioned challenges.
The Ultimate Goal: Proactive AI for Holistic Optimization
While anomaly detection is an important milestone, our broader vision extends far beyond water consumption. We are working toward a Proactive AI system that will collect and analyze data from various onboard sensors, including power consumption, fuel usage, weather conditions, and other critical parameters. By integrating these data streams, AI can provide real-time insights and recommendations, helping operators optimize every aspect of the voyage—from resource efficiency to predictive maintenance. The goal is clear: to make each cruise as efficient and sustainable as possible. The conceptual architecture for such a solution:
- For the sake of simplicity, the architecture demonstrated on the smart devices example can be easily extended to stream and process data from various sources, including images, documents, maintenance records, etc.
- Node-RED is a flow-based programming tool that supports many IoT communication protocols and can easily publish ingested data messages to the streaming platform, such as Apache Kafka.
- With the built-in MLLib library, Spark applies machine learning to detect anomalies in more obvious cases.
- TensorFlow is a software library for ML and AI used for anomaly detection. Deviations from normal operating patterns are detected using autoencoders or LSTM-based models. Complex time-series data, such as vibration, engine sound, fuel usage, etc., can be analyzed by AI models to identify subtle patterns.
- Generative AI: large language models (LLMs) like GPT can parse unstructured text (e.g., maintenance logs, crew notes) and generate richer insights for deep learning process of anomaly detection models. Also, they can be used to translate complex anomaly signals into natural-language explanations for crew members, mitigating the “black box” effect.
By applying language model techniques to ingested data, AI can improve its ability to detect anomalies. This involves recognizing when data deviates from the norm and understanding the context and likely causes of such deviations. This can lead to faster and more accurate diagnoses of potential issues.
Shaping the Future Together
Innovation in maritime AI is not a solitary endeavor—it thrives on collaboration. As we move forward with this initiative, we are actively seeking partners who share our vision for intelligent, data-driven maritime solutions. Together, we can develop the next generation of AI-powered systems that enhance efficiency, reduce environmental impact, and shape the future of the industry.
If you are interested in joining us on this journey, we invite you to connect with us and explore the possibilities of building smarter maritime solutions together.
Andriy Maksymovych
Head of Research & Development
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