Hull Insights

Make confident decisions thanks to increased transparency of your fleets hull condition

Hull Insights is a dashboard providing you with detailed information on the predicted condition of the hull structure of your vessels and fleet. Considering vessel design and construction, data from past surveys and recordings, as well as environmental data, Hull Insights calculates the likelihood for future structural findings in vessel-specific compartment groups.

By digitalizing the hull structure, we aim to give you more control and transparency and help you make informed operational decisions while enhancing the focus on safety and quality of your fleet. 

The degree of insight provided by the tool, enables you to tailor your actions to areas with the highest probability of impact, allowing you to perform more targeted crew inspections and planned maintenance with the benefit of reduced cost and time spent. Hull Insights helps you stay on top of your hull condition, giving you the flexibility to plan ahead and schedule maintenance and inspections. With the overview that Hull Insights provides, you can benchmark the current status of your fleet and develop standards and ambitions for increased safety and efficiency.

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How does Hull Insights work?

Hull Insights is based on a machine learning algorithm. The tool determines the predicted condition of the hull and calculates the probability of weak spots, deriving trends using millions of data streams, that extend far beyond human capabilities.

The following data is being considered in the calculation (historic and real-time):

  • AIS and weather (hindcast), wave height, temperature, wave bending moment, etc.
  • Design and construction such as: yard, class notation, main particulars, owner and manager
  • Surveys and recordings covering: findings, coating condition and maintenance rating of the hull
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“Hull Insights very quickly provides us with an overview of a particular vessel’s historical structural related findings. The tool has huge potential using machine learning to trend structural defects and to assist the ship operator with planning for repair periods. The more data is collected from the different sources, the more value the tool can offer. Altera looks forward to seeing DNV further develop the tool.”

  • James Fowler ,
  • Structural Integrity Manager ,
  • Altera Infrastructure Shuttle and Storage
 

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